Cross-technology Interference Research Articles

Fine-grained frequencies meet synchronous transmission

Fine-grained frequencies have been used for IEEE 802.15.4 based communication in several recent works to enhance network throughput as well as combat Cross Technology Interference (CTI) in licence free ISM bands. However, the works demonstrate that as the Center Frequency Distance (CFD) goes very low (<3 MHz), adjacent channels start severely interfering with each other which heavily limits the use of the fine-grained frequencies. All the studies so far on fine-grained frequencies consider the underlying communication to be based on traditional Asynchronous-Transmission (AT). Synchronous-Transmission (ST) based communication, in contrast, has shown its superiority over AT-based strategies in several different aspects in many recent works. It has been shown that the ability to make efficient use of special physical layer phenomena called Capture-Effect (CE), enables ST-based strategies to carry out more in-parallel communication even under the same channel/frequency (under certain constraints), compared to AT. In this work, we study how this special capability of ST can be exploited in the context of multi-channel or multi-frequency communication with fine-grained frequencies, in particular, when the CFD goes below 3 MHz. We explicitly study how a very narrow band of frequencies can be effectively used for in-parallel communication. Based on extensive local and controlled experiment-based studies, we conclude that the construction of the groups plays a major role in exploiting the fine-grained frequencies. To fulfill the requirements, we design and develop a simple, distributed, and efficient group formation strategy, that can divide a given IoT-network into a desired number of groups and maximize the benefit of the conjunction of ST and the fine-grained frequencies. Through extensive experiment-based evaluations in IoT-testbeds, we show that the groups constructed by the proposed strategy can make very efficient use of the fine-grained frequencies with a CFD as low as 1 MHz to execute in-parallel communication tasks quite reliably and independently. We also demonstrate that even under severe CTI, when the traditional standard channel-based mechanism drastically fails or results in considerably poor performance, communication under fine-grained frequencies under the proposed mechanism can sustain pretty well.

Mitigating Cross-Technology Interference Through Fast Signal Identification

With the prosperity of Internet of Things, a large number of heterogeneous wireless devices share the same unlicensed spectrum, leading to severe cross-technology interference (CTI). Especially, the transmission power asymmetry of heterogeneous devices will further deteriorate this problem, as the high-power devices (like WiFi) always corrupt the ongoing transmissions of low-power devices (like ZigBee), because the energy of the ZigBee signal perceived by WiFi is too weak to achieve the CCA (Clear Channel Assessment) threshold and the channel is considered to be idle. This paper proposes an enhanced CCA (E-CCA) mechanism to mitigate CTI in WiFi and ZigBee coexistence networks. E-CCA contains ZShark which can identify signals within a tolerable time duration. It also contains a MAC layer design, with which a WiFi device can still determine the channel to be busy when the detected energy is below the CCA threshold while a ZigBee signal is identified, thus to avoid CTI to ZigBee transmissions. We set up a testbed based on USRP B200 and TelosB to collect data and evaluate the performance of ZShark. We further evaluate the performance of E-CCA through both theoretical analysis and simulations on NS-3. The results show significant performance improvement of ZigBee.

Real-Time Investigation of Cross-Technology Interference in Heterogeneous IoT Networks

Real-Time Investigation of Cross-Technology Interference in Heterogeneous IoT Networks

D-SCAN: Toward collaborative multi-radio coexistence in mobile devices via deep learning

As the demand for efficient and reliable wireless connectivity continues to increase, mobile Internet-of-Things devices equipped with multiple heterogeneous radios including Wi-Fi and Bluetooth have become prevalent. However, collocated Wi-Fi and Bluetooth operate in the same 2.4 GHz industrial, scientific, and medical band and interfere with each other internally and externally. Although current devices avoid internal cross-technology interference by enabling either Wi-Fi or Bluetooth to transmit packets at any specific time slot in a time-division multiplexing manner, the dissonance with external interference mitigation schemes can result in severe performance degradation. In this paper, we present D-SCAN, a novel collaborative coexistence mechanism. D-SCAN infers nearby Wi-Fi information efficiently using a collocated Bluetooth radio, thereby offsetting the overhead of key Wi-Fi functions and preventing collisions between Wi-Fi and Bluetooth. To this end, D-SCAN adopts a data-driven approach that captures the unique temporal and spectral features of Wi-Fi signals from Bluetooth spectrum measurements by leveraging deep neural networks. A D-SCAN prototype in real-world experiments reduces the latency and energy consumption of legacy Wi-Fi scanning by 23% and 45%, respectively. It also promotes the agile interference avoidance of Bluetooth that coexists with Wi-Fi on a single device. Thus, D-SCAN demonstrates efficiency in resource management and effectiveness in mitigating cross-technology interferences.

Wearable-User Authentication via Cross-Technology Interference in Heterogeneous Environments

The increasing deployment of wireless sensors enables a broad spectrum of health-related wearable applications. Due to the sensitivity of collected personal health information, these wearables should be authenticated together with their users as “wearable-user pairs” to ensure that they are attached to legitimate users. However, various devices are equipped with dedicated sensing abilities and wireless protocols corresponding to data characteristics in practice. Traditional authentication methodologies may not work in this heterogeneous environment because of protocol incompatibility. For example, how to verify a new ZigBee-enabled monitor when the existing trusted device is Wi-Fi-enabled? Therefore, to achieve authentication across protocols, in this article, we leverage the unique cross-technology interference (CTI), triggered by heterogeneous wireless transmissions, along with human physiological activity measurements (e.g., respiration patterns) to design an authentication scheme between wearables and users. Specifically, the authentication from an unknown ZigBee wearable to a trusted Wi-Fi device is achieved by monitoring the channel state information (CSI) changes according to human respiration. Our approach not only successfully recognizes a legitimate wearable-user pair but also blocks illegal access from adversaries. Extensive experiments have been conducted to demonstrate both the security and feasibility of the proposed scheme. The designed mechanism can achieve over 92% authentication accuracy with human subjects.

High-Throughput Cross-Technology Communication via Chip-Level Side Channel

Cross-technology Communication (CTC) is a novel technique enabling data transmission among devices equipped with different physical layers in a heterogeneous wireless network. Particularly, by enabling cross-technology communication between ZigBee and WiFi, the energy-saving problem and media access control under cross-technology interference would be resolved easily. However, the current CTC designs are either too slow or destructive to ongoing ZigBee transmissions. In this paper, we propose a new Cross-technology Communication framework based on ZigBee chip-level side channel. The proposed design enables the high-throughput ZigBee-to-WiFi cross-technology communication while maintaining transparency to the existing ZigBee network. Specifically, we modify some ZigBee chips within one ZigBee symbol to create a side channel at the CTC sender (ZigBee) for carrying CTC bits while the CTC receiver (WiFi) can detect those modifications and demodulate the CTC bits at WiFi preamble detection. Both theoretical analysis and experiments show that our design would be transparent to ZigBee and throughput can reach 31.25 Kbps—1000× the state-of-the-art CTC design.

Enabling WLAN and WPAN Coexistence via Cross-Technology Communication.

In the limited frequency spectrum shared by various wireless communication technologies, cross-technology interference is an important factor which determines communication performance. A variety of coexistence methods to reduce the impact of this interference have been studied, but most of them cannot explicitly coordinate the shared spectrum and are not practical. This paper presents an explicit coexistence mechanism using cross-technology communication among heterogeneous wireless technologies. This mimics “carrier sense multiple access with collision avoidance” (CSMA/CA) via bidirectional cross-technology communication, which is called CTC-CSMA/CA. It allows communication between heterogeneous wireless technologies in order to achieve CSMA/CA. This accurately assigns required channel resources by directly sending and receiving feedback. CTC-CSMA/CA is a highly compatible technology because it does not require any modification to the IEEE 802.11 standard or any extra hardware. In addition, Zigbee can operate with a low duty cycle by synchronizing it to a periodic Wi-Fi beacon. We implemented CTC-CSMA/CA using a commodity Wi-Fi access point and a commercial Zigbee platform. Our experiments showed that the channels are coordinated more accurately by our method, which significantly improves Zigbee throughput, than by conventional schemes. We expect the proposed scheme to be an important application case in designing future cross-technology communication.

Deep Learning-Guided Jamming for Cross-Technology Wireless Networks: Attack and Defense

Wireless networks of different technologies may interfere with each other when they are deployed at proximity. Such cross-technology interference (CTI) has become prevalent with the surge of IoT devices. In this paper, we exploit CTI in coexisting WiFi-Zigbee networks and propose DeepJam, a new stealthy jamming strategy, to jam Zigbee traffic. DeepJam relies on deep learning techniques to capture the temporal pattern of the past wireless traffic and predict the future wireless traffic. By only jamming the victim’s transmissions that are not disrupted by CTI, DeepJam can significantly reduce the victim’s throughput with far fewer jamming signals and is thus much more stealthy than conventional jamming strategies. Detailed evaluations show that DeepJam can converge within 10 sec and achieve the jamming-efficiency gains of up to 742% and 285% over conventional random and reactive jamming strategies, respectively, in practical scenarios. We also propose a simple yet effective countermeasure against DeepJam.

Channel Quality Prediction for TSCH Blacklisting in Highly Dynamic Networks: A Self-Supervised Deep Learning Approach

Cross-Technology Interference (CTI) is a severe issue for wireless networks operating in the license-free ISM bands. In particular, CTI can significantly affect the performance of low-power wireless links used in the Internet-of-Things (IoT). The IEEE 802.15.4 standard adopts a channel hopping scheme in its Time-Slotted Channel Hopping (TSCH) mode as a means to mitigating the adverse effect of CTI. However, the indiscriminate procedure by which TSCH nodes hop over different channels can suffer from severe interference in specific channels. Adaptive channel blacklisting is a technique to alleviate this issue by leaving out low-quality channels from the hopping list. To enable an effective blacklisting, especially in highly varying networks, an accurate real-time prediction of the quality of all available channels is of paramount importance. Previous studies rely on the past records of the channels as an indication of their quality in near future. Evidently, such approaches cannot extend to highly dynamic environments. This paper present a self-supervised approach for training deep neural networks capable of predicting the future behaviour of the frequency channels. The trained models can then substitute the quality assessment approaches in blacklisting schemas. Considering in-vehicle wireless networks as a target application, we evaluate this idea using a real-world experimental dataset, consisting of three measurement scenarios inside a moving vehicle. The experimental results show that using the proposed technique for TSCH blacklisting significantly improves the reliability of networks experiencing such highly dynamic interference and performs at least as good as the existing channel assessment methods in low-interference conditions.

Coexistence Analysis of Co-Located BLE and IEEE 802.15.4 TSCH Networks

Bluetooth Low Energy (BLE) and IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) are widely used low-power standard technologies developed for short-range communications in the internet-of-things. In many applications, BLE and TSCH networks may be deployed in the vicinity of one another, which may lead to Cross Technology Interference (CTI) influencing each other's performance. Both technologies utilize channel hopping to alleviate the impact of external interferences. However, having a model to quantitatively estimate the performance of coexisting TSCH and BLE networks and analyze the role of networks' configuration settings is still an open problem. To address this, we provide a probabilistic analysis of collision-free communication of coexisting BLE and TSCH networks. Moreover, a fast coexistence simulation model is developed that computes the ratio of collision-free transmissions. This model is used to investigate how the performance of the coexisting networks may deviate from the results of the probabilistic analysis. It gives the designers a proper estimation of the worst case performance degradation due to such coexistence. It is shown that severity of the impact of coexisting BLE-TSCH networks on one another depends on the setup configurations and the relative timing of the two networks. The results show that these two technologies can coexist well with collision-free ratios of more than 92.58% and 96.29% in the tested configurations for TSCH and BLE, respectively.

WLAN interference signal recognition using an improved quadruple generative adversarial network

Cross-technology interference sources poses a great challenge for improving throughputs of wireless local area networks (WLAN) and wireless interference signal recognition (WISR) can provide a precondition for mitigating this problem. The quadruple generative adversarial network (QGAN) has shown its prevailing performance for specific emitter identification (SEI). In this paper, an enhanced collaborative learning mechanism is proposed to improve QGAN's performance for WISR. ACGAN is involved in the Improved-QGAN architecture to substitute original GAN, and loss functions are further optimized for generative, representation and classification sub-networks. Besides, a lightweight model based on knowledge distillation (KD) is presented to reduce memory consumption and computational complexity at inference phase. Numerical results indicate that the proposed Improved-QGAN outperforms the other baseline algorithms both on the experimental dataset and benchmark dataset.

Evaluation of the Quality of Aerial Links in Low-Power Wireless Sensor Networks

A wireless sensor network (WSN) assisted by Unmanned Aerial Vehicles (UAVs) can be used to monitor various phenomena in remote, extensive, inaccessible, or dangerous places. The WSN on the ground can provide close-to-the-scene sensing, in-network data processing, and multi-hop communication. The UAVs can interface the ground network with a remote control station or facilitate fast and flexible data collection. To this end, the aerial links established between the UAVs and the WSN are critical. The reliability of the links depends on many factors, including Cross Technology Interference (CTI), the relative distance of the UAVs from the ground nodes, the drive quality of the UAVs, and noise. In this paper, we present experimental results addressing some of these issues. Our experiments consisted of eleven IEEE 802.15.4 compliant transceivers, nine of which were deployed on the ground in a grid topology with a further two being attached to a UAV. From careful examination of traces extracted from received packets we concluded that CTI is the most significant factor affecting the quality of aerial links. Our observations have application for deployment related decision-making and for the design of UAV-assisted data collection protocols.

BuSAR: Bluetooth Slot Availability Randomization for Better Coexistence With Dense Wi-Fi Networks

The last decade has witnessed the ever-increasing deployment of Wi-Fi networks and the explosion of Bluetooth-based applications. As a result, the coexistence of Bluetooth piconets with highly-dense Wi-Fi networks is a common phenomenon currently. Unlike Wi-Fi that conducts carrier sensing before channel access, Bluetooth adopts frequency hopping based on a predefined hop sequence, which inevitably incurs considerable cross-technology interference to Wi-Fi. While the Adaptive Frequency Hopping technique is standardized for interference reduction, it does not perform well in current practice where densely-deployed Wi-Fi networks commonly cover the whole 2.4 GHz unlicensed spectrum. In this context, this article presents BuSAR, a novel approach to account for the coexistence problem between Bluetooth piconets and dense Wi-Fi networks. BuSAR embodies the first work to aim at mitigating the cross-technology interference between Bluetooth and highly-dense Wi-Fi networks in a distributed manner. At the heart of BuSAR lies a subtle technique called B l u etooth s lot a vailability r andomization, which exploits the redundancy of erroneous Bluetooth packets for better Bluetooth/Wi-Fi coexistence. With BuSAR adopted, multiple Bluetooth piconets are guaranteed to operate independently and only a lightweight algorithm is needed to be implemented at each Bluetooth device. Both theoretical analysis and experimental results validate the feasibility and superiority of BuSAR.

CoHop

Cross-Technology Interference (CTI) badly harms the transmission reliability for low-power networks such as ZigBee at 2.4-GHz band. Though promising, channel hopping still faces challenges because the increasingly dense deployment of CTI leaves very few available channels. Selecting a good channel with the least overhead is crucial but challenging. Most of the existing works are heuristic methods that choose a channel far from the current one to avoid adjacent channels that may be correlatively interfered by CTI with a wider bandwidth such as WiFi. However, we observe that the correlated channels influenced by the same CTI source do not necessarily have the same channel qualities and even the opposite state, due to the uneven spectrum power density of CTI. Such channel opportunities are unexplored and wasted. In this article, we propose CoHop, a quantitative correlation-based channel hopping method for low-power wireless networks. We establish a quantitative model that describes the correlation of channel qualities to capture channel opportunities and calculate channel quality without probing, to reduce probing overhead. The probing sequence is optimized based on the Pearson Correlation Coefficient and the prediction-based probing algorithm. We implement CoHop on TinyOS and evaluate its performance in various environments. The experimental results show that CoHop can increase the Packet Reception Ratio by 80%, compared with existing methods.

Autonomous Interference Mapping for Industrial Internet of Things Networks Over Unlicensed Bands: Identifying Cross-Technology Interference

The limited coexistence capabilities of current Internet-of-things (IoT) wireless standards produce inefficient spectrum utilization and mutual performance impairment. The entity of the issue escalates in industrial IoT (IIoT) applications, which instead have stringent quality-of-service requirements and exhibit very-low error tolerance. The constant growth of wireless applications over unlicensed bands mandates then the adoption of dynamic spectrum access techniques, which can greatly benefit from interference mapping over multiple dimensions of the radio space. In this article, the authors analyze the critical role of real-time interference detection and classification mechanisms that rely on IIoT devices only, without the added complexity of specialized hardware. The trade-offs between classification performance and feasibility are analyzed in connection with the implementation on low-complexity IIoT devices. Moreover, the authors explain how to use such mechanisms for enabling IIoT networks to construct and maintain multidimensional interference maps at run-time in an autonomous fashion. Lastly, the authors give an overview of the opportunities and challenges of using interference maps to enhance the performance of IIoT networks under interference.

Enabling Global Cooperation for Heterogeneous Networks via Reliable Concurrent Cross Technology Communications

Industrial Scientific Medical (ISM) band has become more and more crowded due to the ever-growing size of many mainstream technologies, e.g., Wi-Fi, ZigBee and Bluetooth. Though the coexistence issue has led to severe Cross Technology Interference (CTI), it provides great opportunities to better utilize the scarce bandwidth resources. A fundamental question is how to ensure harmonious and effective operations for different networks To exploit it, a novel global cooperation framework is proposed. In particular, our work enables reliable concurrent Cross Technology Communication (CTC) from Wi-Fi to Wi-Fi, ZigBee and Bluetooth commodity devices. Compared to existing CTC approaches, our scheme improves the communication efficiency significantly, and hence is the foundation for effective global cooperation. Based on the proposed CTC scheme, a unified Media Access Control (MAC) approach is introduced to cooperate CTC message transmission and reception for heterogeneous networks with different MACs. Three proof-of-concepts applications, e.g. global synchronization, global CTI coordination and global Location Based Service (LBS) broadcasting are discussed to fully leverage the benefits of global cooperation. Extensive evaluations show that compared with existing schemes, the proposed framework achieves 8.1 times lower synchronization error, 9 times lower packet delivery delay, and 2.7 times lower energy consumption for acquiring LBS message.

Vulnerability of WiFi’s Noise Floor Calibration

Most wireless technologies, including WiFi, rely heavily on clear channel assessment (CCA) to avoid collisions, not only among the devices within the same technology but also against cross-technology interference. If the CCA threshold is not configured properly, both the transmission and reception performance will be seriously affected with behaviors unexpected from the protocol's perspective. On the other hand, WiFi uses an adaptive CCA threshold based on a noise floor (NF) calibration algorithm to account for ambient NF changes and slight differences in hardware. However, this turns out to be a serious vulnerability of WiFi. In this work, we show that one can easily generate a wireless signal that can take advantage of the NF calibration algorithm of WiFi to inflate the CCA threshold and degrade performance significantly. The signal can be generated from a commercial off-the-shelf ZigBee device, and if well designed, need not be too long nor strong. We show that WiFi is vulnerable to such attack, and more surprisingly, the network performance does not recover long after the signal disappears. We exemplify and verify our findings through extensive real-world experiments using five types of commercial WiFi NICs and three different WiFi access points to show that this is a critical problem that exists in reality and must be addressed.

A Novel Spread Spectrum and Clustering Mixed Approach with Network Coding for Enhanced Narrowband IoT (NB-IoT) Scalability

The Narrowband Internet of Things (NB-IoT) is a very promising licensed Internet of things (IoT) technology for accommodating massive device connections in 5G and beyond. To enable network scalability, this study proposes a two-layers novel mixed approach that aims not only to create an efficient spectrum sharing among the many NB-IoT devices but also provides an energy-efficient network. On one layer, the approach uses an Adaptive Frequency Hopping Spread Spectrum (AFHSS) technique that uses a lightweight and secure pseudo-random sequence to exploit the channel diversity, to mitigate inter-link and cross-technology interference. On the second layer, the approach consists of a clustering and network coding (data aggregation) approach based on an energy-signal strength mixed gradient. The second layer contributes to offload the BS, allows for energy-efficient network scalability, helps balance the energy consumption of the network, and enhances the overall network lifetime. The proposed mixed strategy algorithm is modelled and simulated using the Matrix Laboratory (MATLAB) Long Term Evolution (LTE) toolbox. The obtained results reveal that the proposed mixed approach enhances network scalability while improving energy efficiency, transmission reliability, and network lifetime when compared to the existing spread spectrum only, nodes clustering only, and mixed approach with no network coding approaches.

PolarScout: Wi-Fi Interference-Resilient ZigBee Communication via Shell-Shaping

The prosperity of IEEE 802.11-based Wi-Fi networks aggravates cross-technology interference to IEEE 802.15.4-enabled ZigBee networks widely deployed to enable various Internet-of-Things applications. To make ZigBee communication reliable and robust even in a dense Wi-Fi environment, taming Wi-Fi interference in ZigBee networks especially from the perspective of physical layer is of paramount importance. In this context, this work takes aim to design a novel Wi-Fi interference-resilient ZigBee decoder called PolarScout, which separates collided ZigBee signal samples out of Wi-Fi interference to bootstrap ZigBee data decoding. Unlike several existing solutions which need clear signal preamble, tremendous signal strength difference between ZigBee and Wi-Fi, and Wi-Fi interference recognition in prior to ZigBee decoding, PolarScout aims at direct ZigBee decoding in a more generic and challenging case where Wi-Fi interference features a wide range of power levels and arises within a ZigBee packet at an arbitrary position. At the heart of PolarScout lies a subtle shell-shaping technique which harnesses a customized sample sequence to smooth the shell of corrupted signal samples. PolarScout then refers to the resulting shell to recover each contaminated ZigBee sample. Experimental results validate the superiority of PolarScout and its resilience to a wide range of Wi-Fi interference types.

Periodicity makes perfect: recovering interfered preamble for high-coexistence wireless systems

Recent researches show that cross technology interference management plays an important role in the emergence of innovative applications for nowadays wireless networks. Existing algorithms either suffer from the constraint on the need of clean preamble or require technological similarities among cross technology systems. In this paper, we ask the question: “whether the packet with interfered preamble can be decoded?" To answer this question, we investigate the property of preamble and propose Narthil, an innovative coexistence algorithm which based on imperfect interference management, handling packet detection, symbol synchronization, and channel estimation with interfered preambles. Narthil applies a simple Butterworth band-stop filter for imperfect interference filtering, and leverages the residual signals for packet detection and symbol synchronization, as well as channel estimation. The insight is the inherent properties of preamble such as periodicity and modulation scheme, and the frequency continuity in OFDM signal could be effectively leveraged for important processes such as packet detection, symbol synchronization, and channel estimation. These favorable properties could be used to further enhance the performance of other cross technology signal coexistence algorithms. We implement Narthil algorithm on our USRP/GNU Radio platform, and evaluate its performance by using 15 USRP-N210 devices through the real deployment experiments. The results demonstrate that Narthil can effectively detect the incoming packet and achieves a high accuracy symbol synchronization, as well as channel estimation of the interfered preamble, and also achieve a good performance on packet decoding.